820 citations · 12k across the 115 of their papers we have counts for
15 papers · 1 filter
Improving Sample and Feature Selection with Principal Covariates Regression
Rose K. Cersonsky, Benjamin A. Helfrecht, Edgar A. Engel +1
Selecting the most relevant features and samples out of a large set of candidates is a task that occurs very often in the context of automated data analysis, where it can be used t…
Machine learning at the atomic-scale
Félix Musil, Michele Ceriotti
Statistical learning algorithms are finding more and more applications in science and technology. Atomic-scale modeling is no exception, with machine learning becoming commonplace…
Finite-temperature materials modeling from the quantum nuclei to the hot electrons regime
Nataliya Lopanitsyna, Chiheb Ben Mahmoud, Michele Ceriotti
Atomistic simulations provide insights into structure-property relations on an atomic size and length scale, that are complementary to the macroscopic observables that can be obtai…
Uncertainty estimation for molecular dynamics and sampling
Giulio Imbalzano, Yongbin Zhuang, Venkat Kapil +4
Machine learning models have emerged as a very effective strategy to sidestep time-consuming electronic-structure calculations, enabling accurate simulations of greater size, time…
Global free energy landscapes as a smoothly joined collection of local maps
F. Giberti, G. A. Tribello, M. Ceriotti
Enhanced sampling techniques have become an essential tool in computational chemistry and physics, where they are applied to sample activated processes that occur on a time scale t…
The role of feature space in atomistic learning
Alexander Goscinski, Guillaume Fraux, Giulio Imbalzano +1
Eficient, physically-inspired descriptors of the structure and composition of molecules and materials play a key role in the application of machine-learning techniques to atomistic…